--- library_name: transformers license: apache-2.0 license_link: https://github.com/foreverlasting1202/QuestA/blob/main/LICENSE pipeline_tag: text-generation --- # QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation

| Paper | Documentation | Blog | 🤗Models | 🤗Datas | ## Highlights QuestA introduces **question augmentation** to significantly improve reasoning tasks in large language models (LLMs). By incorporating partial solutions during reinforcement learning (RL) training, QuestA enhances problem-solving capacity and accelerates learning on challenging tasks. Key improvements with **QuestA**: - **Significant performance boost** on math reasoning benchmarks (e.g., AIME25, HMMT25), including a **10%+ increase** in accuracy. - **Enhanced training efficiency** via augmented prompts, allowing more tractable learning on hard problems. - **State-of-the-art results** for 1.5B-parameter models, making QuestA effective even on models with smaller parameter sizes. ![benchmarks](https://cdn-uploads.huggingface.co/production/uploads/6740b458116d17ed148be1e3/JUT4yQ_U5Wm8A7c_at1px.png) ![pass_full](https://cdn-uploads.huggingface.co/production/uploads/6740b458116d17ed148be1e3/Ix-WKPAFScqmzC83Uz6qw.png) ## Model Overview - **Model Type**: Causal Language Model (RL-based Training) - **Training Method**: Reinforcement Learning (RL) with Question Augmentation - **Number of Parameters**: 1.5B (base model), augmented with dynamic difficulty control - **Layer Count**: Customizable based on the RL training configuration - **Context Length**: 32K tokens (configurable) - **Main Innovation**: Question Augmentation with Partial Solutions QuestA dynamically adjusts problem difficulty by providing partial solutions to complex problems, thus improving the model’s ability to solve hard tasks more effectively. ## Performance QuestA achieves the following performance improvements over baseline models, particularly in the field of math reasoning: | Model | AIME24 | AIME25 | HMMT FEB 25 | Olympiad Bench | BRUMO25 | Avg. | | ----------------------- | -------- | -------- | ----------- | -------------- | -------- | -------- | | DeepSeek-R1-Distill-32B | **72.6** | 51.8 | 33.0 | 65.0 | 68.0 | 58.1 | | Qwen3-1.7B | 48.3 | 36.8 | 22.2 | 56.1 | 44.1 | 41.5 | | Nemotron-1.5B | 61.8 | 49.5 | 31.6 | 64.6 | 58.2 | 53.1 | | *QuestA*-Nemotron-1.5B | 72.5 | **62.3** | **41.7** | **70.4** | **69.5** | **63.3** | - **Pass@k Performance**: Shows consistent improvement across various difficulty levels. ## Quickstart To get started with QuestA, you can load the model using the `transformers` library. Make sure you have the latest version installed. ```bash pip install transformers ``` Example Python code to run QuestA: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "QuestA/QuestA-Nemotron-1.5B" # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) # Generate response with augmented question prompt = "Solve for x: 2x + 3 = 11." inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs) # Decode the response response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` For deployment, QuestA can be served using frameworks like **vLLM** or **SGLang**: ```bash # For vLLM vllm serve QuestA/QuestA-Nemotron-1.5B --tensor-parallel-size 8 --max-model-len 32768 ``` ## Key Features - **Question Augmentation**: Prepend partial solutions to difficult problems, aiding model learning. - **Curriculum-based RL**: Gradually reduce dependency on hints as training progresses. - **Training with Augmented Data**: Use dynamically filtered datasets to focus on the hardest problems. - **Efficient Learning**: Faster convergence on complex tasks due to better sampling and more informative rewards. ## Citation If you find this work useful, please cite our paper: ```bibtex @misc{li2025questaexpandingreasoningcapacity, title={QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation}, author={Jiazheng Li and Hong Lu and Kaiyue Wen and Zaiwen Yang and Jiaxuan Gao and Hongzhou Lin and Yi Wu and Jingzhao Zhang}, year={2025}, eprint={2507.13266}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2507.13266}, } ``` For more details on the methodology, results, and code, visit the official [QuestA GitHub repository](https://github.com/foreverlasting1202/QuestA). ## Conclusion QuestA is a novel framework for enhancing LLMs' reasoning capabilities by addressing complex problems more effectively. By augmenting the training process with partial solutions, QuestA accelerates learning, resulting in state-of-the-art performance on benchmark math reasoning tasks and more.